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Introduction to the ML System Design Interview

The ML system design interview assesses your ability to apply ML knowledge to a real-world setting. At a high level, think of your interview as a presentation you’d use to convince the team (which may include non-ML engineers) that your model should be launched into production.

This course covers the fundamentals of ML system design and shows you how to effectively communicate your design through an interview framework. In this lesson, we give an overview of the interview round, what to expect, and how to prepare.

ML system design vs. system design

ML system design problems are fundamentally different from standard system design problems. ML system design questions tend to be more specific to your ML background than general system design questions. Most of the key components of ML systems that don’t appear in standard system design interviews include topics like data engineering and models.

However, some aspects of standard system design interviews apply to ML system design interviews, including discussions of data models and frameworks, scaling, and demonstrating strong communication skills.

What to expect

Example questions include:

These interviews are particularly important for L4+, or mid to senior-level, ML engineer candidates. However, some startups (particularly ML-focused ones) may conduct ML system design interviews for L3 ML enginer candidates, usually centered around a problem they’re working on. For a mid to senior-level individual contributor (IC), this interview round will strongly influence the IC’s specific level placement.

Questions from FAANG vs. startups

ML interviews at a startup tend to be more focused on the company's mission. There’s usually some high-level business element that you want to integrate into your solution. For example, if the company focuses on unsupervised methods, this should be a part of your design focus.

A FAANG interview is likely to ask fewer team or application-specific questions, since the interviewer may not be directly involved with the team you’re interviewing for. For example, a FAANG interviewer might ask a generic image search question, while a startup interviewer might ask you a question that’s tightly coupled with the team’s focus. Additionally, large companies like FAANGs have massive datasets, so they confront more projects and challenges related to scaling systems. Therefore, you’re more likely to receive follow-up questions about handling systems at scale in FAANG+ interviews.

Questions by level

The core problems typically don’t change by level, but what the interviewer expects you to cover will vary, depending on your level. The evidence used to determine your level includes the number of unprompted hints, guidance, and clarifying questions necessary to get an acceptable design. The interviewer will continue to help narrow the scope as you work through it, depending on how well you are progressing.

What interviewers look for

The interviewer isn’t looking for the “one” solution; rather, they want to see how you think and communicate. More specifically, you’re assessed on how well you:

  • Define specific challenges (e.g. dataset, labeling, model execution, required performance) from ambiguous system and/or product requirements.
  • Evaluate different models, weighing the tradeoffs each to reach a working solution.
  • Identify runtime concerns, potential risks around data privacy, dependencies on upstream models, and/or influences on downstream models.
  • Design a solution that would run successfully on real-world data, not just on datasets existing in a controlled environment.

For an in-depth look into how interviewers assess candidates, review our ML system design rubric.

How to prepare

With ML interview prep, there’s a lot of information to cover. Focus on getting a high-level intuitive understanding first, then study the implementation details. Ultimately, it’s most important to understand why certain design decisions are made, and how you can generalize those problem-solving techniques to other problems.

Check out the following resources to gain the high-level and implementation knowledge:

Lastly, try to connect with ML industry peers you know, or reach out to some in our Slack community to have open-ended machine learning discussions. These discussions will help you think more holistically about machine learning concepts and have a wider perspective that will directly improve your interview performance.

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